gn builders quick lab note: verified JSTprove receipts inside a mobile wallet offline verifier ~150ms, zero network calls, UX unchanged @inference_labs $INF https://t.co/UVu2QaVgc1
gn builders quick lab note: verified JSTprove receipts inside a mobile wallet offline verifier ~150ms, zero network calls, UX unchanged @inference_labs $INF https://t.co/UVu2QaVgc1
afternoon builders quick note
����������‑���������� ������������������: wired a JSTprove receipt to a Warden‑gated pay‑per‑inference royalty so model owners get instant, auditable royalties @inference_labs turns usage into enforceable IP payments #verifiableAI $INF
Afternoon builders quick lab note: I prototyped a consent-chain this week that couples JSTprove receipts with a user consent token and a policy hash so every inference carries two verifiable facts: the exact data slice used (privacy-hashed) and the legal basis that allowed that slice to be consumed. plugged it into a demo healthcare pipeline when a model requested a sensitive feature the verifier either emitted a compact consent-proof or blocked the call with an explainable failure subproof. auditors could validate consent timelines without ever seeing raw PII
result: regulators get math, product teams get unblockable UX (consent revocation is immediate and provable), and marketplaces can list sensitive datasets with built-in, auditable usage guards. this feels like a new primitive for cross-jurisdiction data flows and responsible monetization
if proofs can attest "what decision was made", can they also prove "who okayed the data and under what rule"? would your compliance or privacy team trust a zk consent receipt as evidence, or do we need new legal wrappers first @inference_labs #verifiableAI #privacy #zkML $INF